Sample selection for statistical grammar induction
Published 1 January 2000Open access
Rebecca Hwa
Citations69
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TL;DR
It is shown that the amount of annotated training data can be reduced by 36% without degrading the quality of the induced grammars.
Abstract
Corpus-based grammar induction relies on using many hand-parsed sentences as training examples. However, the construction of a training corpus with detailed syntactic analysis for every sentence is a labor-intensive task. We propose to use sample selection methods to minimize the amount of annotation needed in the training data, thereby reducing the workload of the human annotators. This paper shows that the amount of annotated training data can be reduced by 36% without degrading the quality of the induced grammars.
Keywords
Computer Science
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